An emerging approach in the field of aircraft engine controls and health management is the inclusion of real-time on-board models for the in-flight estimation of engine performance variations. This technology, typically based on Kalman filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics and health management applications. A challenge which complicates this practice is the fact that an aircraft engine's performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as parameters to be estimated. However, in an aircraft engine the number of sensors available is typically less than the number of health parameters presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a sub-set of the health parameters, referred to as model tuning parameters. While this approach enables on-line Kalman filter-based estimation, it can result in “smearing” the effects of unestimated health parameters onto those which are estimated, and in turn introduce error in the accuracy of overall model-based performance estimation applications.
Recently, a new method has been presented based on singular value decomposition that selects a model tuning parameter vector of low-enough dimension to be estimated by a Kalman filter. The model tuning parameter vector, defined as q, was constructed as a linear combination of all health parameters, h, given by the equationq=V*h,   (1)where the transformation matrix, V*, is selected applying singular value decomposition to capture the overall effect of the larger set of health parameters on the engine variables as closely as possible in the least squares sense.